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Tracking topic evolution via salient keyword matching with consideration of semantic broadness for Web video discovery

Authors:

Ryosuke Harakawa,

Takahiro Ogawa,

Miki HaseyamaAuthors Info & Claims

Multimedia Tools and Applications, Volume 77, Issue 16

Pages 20297 - 20324

https://doi.org/10.1007/s11042-017-5404-4

Published: 01 August 2018 Publication History

Abstract

A method to track topic evolution via salient keyword matching with consideration of semantic broadness for Web video discovery is presented in this paper. The proposed method enables users to understand the evolution of topics over time for discovering Web videos in which they are interested. A framework that enables extraction and tracking of the hierarchical structure, which contains Web video groups with various degrees of semantic broadness, is newly derived as follows: Based on network analysis using multimodal features, i.e., features of video contents and metadata, our method extracts the hierarchical structure and salient keywords that represent contents of each Web video group. Moreover, salient keyword matching, which is newly developed by considering salient keyword distribution, semantic broadness of each Web video group and initial topic relevance, is applied to each hierarchical structure obtained in different time stamps. Unlike methods in previous works, by considering the semantic broadness as well as the salient keyword distribution, our method can overcome the problem of the desired semantic broadness of topics being different depending on each user. Also, the initial topic relevance enables correction of the gap from an initial topic at the start of tracking. Consequently, it becomes feasible to track the evolution of topics over time for finding Web videos in which the users are interested. Experimental results for real-world datasets containing YouTube videos verify the effectiveness of the proposed method.

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Cited By

Nir OVidra INeeman AKinarti BShamir A(2024)VCR: Video representation for Contextual RetrievalProceedings of the International Conference on Computing, Machine Learning and Data Science10.1145/3661725.3661766(1-9)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3661725.3661766

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cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 77, Issue 16

August 2018

1520 pages

ISSN:1380-7501

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Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 August 2018

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Cited By

Nir OVidra INeeman AKinarti BShamir A(2024)VCR: Video representation for Contextual RetrievalProceedings of the International Conference on Computing, Machine Learning and Data Science10.1145/3661725.3661766(1-9)Online publication date: 12-Apr-2024
https://dl.acm.org/doi/10.1145/3661725.3661766

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